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Jakobsen RS, Nielsen TD, Leutscher P, Koch K. A study on the risk stratification for patients within 24 hours of admission for risk of hospital-acquired urinary tract infection using Bayesian network models. Health Informatics J 2024; 30:14604582241234232. [PMID: 38419559 DOI: 10.1177/14604582241234232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/02/2024]
Abstract
Early identification of patients at risk of hospital-acquired urinary tract infections (HA-UTI) enables the initiation of timely targeted preventive and therapeutic strategies. Machine learning (ML) models have shown great potential for this purpose. However, existing ML models in infection control have demonstrated poor ability to support explainability, which challenges the interpretation of the result in clinical practice, limiting the adaption of the ML models into a daily clinical routine. In this study, we developed Bayesian Network (BN) models to enable explainable assessment within 24 h of admission for risk of HA-UTI. Our dataset contained 138,250 unique hospital admissions. We included data on admission details, demographics, lifestyle factors, comorbidities, vital parameters, laboratory results, and urinary catheter. Models developed from a reduced set of five features were characterized by transparency compared to models developed from a full set of 50 features. The expert-based clinical BN model over the reduced feature space showed the highest performance (area under the curve = 0.746) compared to the naïve- and tree-augmented-naïve BN models. Moreover, models developed from expert-based knowledge were characterized by enhanced explainability.
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Affiliation(s)
- Rune Sejer Jakobsen
- Centre for Clinical Research, North Denmark Regional Hospital, Aalborg, Denmark
- Business Intelligence and Analysis, The North Denmark Region, Aalborg, Denmark
| | | | - Peter Leutscher
- Centre for Clinical Research, North Denmark Regional Hospital, Aalborg, Denmark
- Department of Clinical Medicine, Aalborg Universitet, Aalborg, Denmark
| | - Kristoffer Koch
- Centre for Clinical Research, North Denmark Regional Hospital, Aalborg, Denmark
- Department of Clinical Microbiology, Aalborg University Hospital, Aalborg, Denmark
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Saucier JA, Dietrich MS, Maxwell C, Lane-Fall MB, Minnick A. Trauma Patient Transitions From Critical Care: A Survey of U.S. Trauma Centers. J Trauma Nurs 2023; 30:318-327. [PMID: 37937871 DOI: 10.1097/jtn.0000000000000750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2023]
Abstract
BACKGROUND Transitions between clinical units are vulnerable periods for patients. A significant body of evidence describes the importance of structured transitions, but there is limited reporting of what happens. Describing transitions within a conceptual model will characterize the salient forces that interact during a patient transition and, perhaps, lead to improved outcomes. OBJECTIVE To describe the processes and resources that trauma centers use to transition patients from critical care to nonintensive care units. METHODS This cross-sectional study surveyed all Level I and II trauma centers listed in the American Trauma Society database from September 2020 to November 2020. Data were merged from the American Hospital Association 2018 Hospital Survey. RESULTS A total of 567 surveys were distributed, of which 152 responded for a (27%) response rate. Results were organized in categories: capital input, organizational facets, employee behavior, employee terms/scope, and labor inputs. Resources and processes varied; the most important opportunities for transition improvement included: (1) handoff instruments were only reported at 36% (n = 27) of trauma centers, (2) mandatory resident education about transitions was only reported at 70% (n = 16) of trauma centers, and (3) only 6% (n = 4) of trauma centers reported electronic medical record applications that enact features to influence employee behavior. CONCLUSIONS After years of focusing on transitions as a high-stake period, there remain many opportunities to develop resources and enact effective processes to address the variability in transition practice across trauma centers.
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Affiliation(s)
- Jason A Saucier
- Department of Advanced Practice, Penn Medicine, Philadelphia, Pennsylvania, and Trauma Surgical Critical Care, Vanderbilt University School of Nursing, Nashville, Tennessee (Dr Saucier); Vanderbilt University School of Medicine, and Biostatistics, Hearing, & Speech, Ingram Cancer Center, Vanderbilt School of Nursing, Nashville, Tennessee (Dr Dietrich); Vanderbilt University School of Nursing, Nashville, Tennessee (Drs Maxwell and Minnick); and Perelman School of Medicine, University of Pennsylvania, Philadelphia (Dr Lane-Fall)
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Phang P, Labadin J, Suhaila J, Aslam S, Hazmi H. Exploration of spatiotemporal heterogeneity and socio-demographic determinants on COVID-19 incidence rates in Sarawak, Malaysia. BMC Public Health 2023; 23:1396. [PMID: 37474904 PMCID: PMC10357875 DOI: 10.1186/s12889-023-16300-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 07/12/2023] [Indexed: 07/22/2023] Open
Abstract
BACKGROUND In Sarawak, 252 300 coronavirus disease 2019 (COVID-19) cases have been recorded with 1 619 fatalities in 2021, compared to only 1 117 cases in 2020. Since Sarawak is geographically separated from Peninsular Malaysia and half of its population resides in rural districts where medical resources are limited, the analysis of spatiotemporal heterogeneity of disease incidence rates and their relationship with socio-demographic factors are crucial in understanding the spread of the disease in Sarawak. METHODS The spatial dependence of district-wise incidence rates is investigated using spatial autocorrelation analysis with two orders of contiguity weights for various pandemic waves. Nine determinants are chosen from 14 covariates of socio-demographic factors via elastic net regression and recursive partitioning. The relationships between incidence rates and socio-demographic factors are examined using ordinary least squares, spatial lag and spatial error models, and geographically weighted regression. RESULTS In the first 8 months of 2021, COVID-19 severely affected Sarawak's central region, which was followed by the southern region in the next 2 months. In the third wave, based on second-order spatial weights, the incidence rate in a district is most strongly influenced by its neighboring districts' rate, although the variance of incidence rates is best explained by local regression coefficient estimates of socio-demographic factors in the first wave. It is discovered that the percentage of households with garbage collection facilities, population density and the proportion of male in the population are positively associated with the increase in COVID-19 incidence rates. CONCLUSION This research provides useful insights for the State Government and public health authorities to critically incorporate socio-demographic characteristics of local communities into evidence-based decision-making for altering disease monitoring and response plans. Policymakers can make well-informed judgments and implement targeted interventions by having an in-depth understanding of the spatial patterns and relationships between COVID-19 incidence rates and socio-demographic characteristics. This will effectively help in mitigating the spread of the disease.
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Affiliation(s)
- Piau Phang
- Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, Kota Samarahan, 94300, Sarawak, Malaysia.
| | - Jane Labadin
- Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, Kota Samarahan, 94300, Sarawak, Malaysia
| | - Jamaludin Suhaila
- Department of Mathematical Science, Faculty of Science, Universiti Teknologi Malaysia, Skudai, 81310, Johor, Malaysia
| | - Saira Aslam
- Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, Kota Samarahan, 94300, Sarawak, Malaysia
| | - Helmy Hazmi
- Faculty of Medicine and Health Science, Universiti Malaysia Sarawak, Kota Samarahan, 94300, Sarawak, Malaysia
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Chen M, Fan Y, Xu Q, Huang H, Zheng X, Xiao D, Fang W, Qin J, Zheng J, Dong E. Medical implementation practice and its medical performance evaluation of a giant makeshift hospital during the COVID-19 pandemic: An innovative model response to a public health emergency in Shanghai, China. Front Public Health 2023; 10:1019073. [PMID: 36684897 PMCID: PMC9853970 DOI: 10.3389/fpubh.2022.1019073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Accepted: 12/05/2022] [Indexed: 01/07/2023] Open
Abstract
Introduction In confronting the sudden COVID-19 epidemic, China and other countries have been under great pressure to block virus transmission and reduce fatalities. Converting large-scale public venues into makeshift hospitals is a popular response. This addresses the outbreak and can maintain smooth operation of a country or region's healthcare system during a pandemic. However, large makeshift hospitals, such as the Shanghai New International Expo Center (SNIEC) makeshift hospital, which was one of the largest makeshift hospitals in the world, face two major problems: Effective and precise transfer of patients and heterogeneity of the medical care teams. Methods To solve these problems, this study presents the medical practices of the SNIEC makeshift hospital in Shanghai, China. The experiences include constructing two groups, developing a medical management protocol, implementing a multi-dimensional management mode to screen patients, transferring them effectively, and achieving homogeneous quality of medical care. To evaluate the medical practice performance of the SNIEC makeshift hospital, 41,941 infected patients were retrospectively reviewed from March 31 to May 23, 2022. Multivariate logistic regression method and a tree-augmented naive (TAN) Bayesian network mode were used. Results We identified that the three most important variables were chronic disease, age, and type of cabin, with importance values of 0.63, 0.15, and 0.11, respectively. The constructed TAN Bayesian network model had good predictive values; the overall correct rates of the model-training dataset partition and test dataset partition were 99.19 and 99.05%, respectively, and the respective values for the area under the receiver operating characteristic curve were 0.939 and 0.957. Conclusion The medical practice in the SNIEC makeshift hospital was implemented well, had good medical care performance, and could be copied worldwide as a practical intervention to fight the epidemic in China and other developing countries.
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Affiliation(s)
- Minjie Chen
- Department of Outpatient and Emergency Management, Renji Hospital, School of Medicine in Shanghai Jiao Tong University, Shanghai, China
| | - Yiling Fan
- Department of Neurosurgery, Renji Hospital, School of Medicine in Shanghai Jiao Tong University, Shanghai, China
| | - Qingrong Xu
- Department of Orthopaedics, Renji Hospital, School of Medicine in Shanghai Jiao Tong University, Shanghai, China
| | - Hua Huang
- Department of Administration, Renji Hospital, School of Medicine in Shanghai Jiao Tong University, Shanghai, China
| | - Xinyi Zheng
- Department of Pharmacy, Huashan Hospital, Fudan University, Shanghai, China
| | - Dongdong Xiao
- Department of Urology, Renji Hospital, School of Medicine in Shanghai Jiao Tong University, Shanghai, China
| | - Weilin Fang
- Department of Urology, Renji Hospital, School of Medicine in Shanghai Jiao Tong University, Shanghai, China
| | - Jun Qin
- Department of Gastroenterology, Renji Hospital, School of Medicine in Shanghai Jiao Tong University, Shanghai, China
| | - Junhua Zheng
- Department of Urology, Renji Hospital, School of Medicine in Shanghai Jiao Tong University, Shanghai, China
| | - Enhong Dong
- School of Nursing and Health Management, Shanghai University of Medicine and Health Sciences, Shanghai, China
- Institute of Healthy Yangtze River Delta, Shanghai Jiao Tong University, Shanghai, China
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Machine Learning Models for Early Prediction of Sepsis on Large Healthcare Datasets. ELECTRONICS 2022. [DOI: 10.3390/electronics11091507] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Sepsis is a highly lethal syndrome with heterogeneous clinical manifestation that can be hard to identify and treat. Early diagnosis and appropriate treatment are critical to reduce mortality and promote survival in suspected cases and improve the outcomes. Several screening prediction systems have been proposed for evaluating the early detection of patient deterioration, but the efficacy is still limited at individual level. The increasing amount and the versatility of healthcare data suggest implementing machine learning techniques to develop models for predicting sepsis. This work presents an experimental study of some machine-learning-based models for sepsis prediction considering vital signs, laboratory test results, and demographics using Medical Information Mart for Intensive Care III (MIMIC-III) (v1.4), a publicly available dataset. The experimental results demonstrate an overall higher performance of machine learning models over the commonly used Sequential Organ Failure Assessment (SOFA) and Quick SOFA (qSOFA) scoring systems at the time of sepsis onset.
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Missing Data Imputation – A Survey. INTERNATIONAL JOURNAL OF DECISION SUPPORT SYSTEM TECHNOLOGY 2022. [DOI: 10.4018/ijdsst.292446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Many real world datasets may contain missing values for various reasons. These incomplete datasets can pose severe issues to the underlying machine learning algorithms and decision support systems. It may result in high computational cost, skewed output and invalid deductions. Various solutions exist to mitigate this issue; the most popular strategy is to estimate the missing values by applying inferential techniques such as linear regression, decision trees or Bayesian inference. In this paper, the missing data problem is discussed in detail with a comprehensive review of the approaches to tackle it. The paper concludes with a discussion on the effectiveness of three imputation methods namely, imputation based on Multiple Linear Regression (MLR), Predictive Mean Matching (PMM) and Classification And Regression Tree (CART) in the context of subspace clustering. The experimental results obtained on real benchmark datasets and high-dimensional synthetic datasets highlight that, MLR based imputation method is more efficient on high-dimensional incomplete datasets.
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Hassan N, Slight R, Weiand D, Vellinga A, Morgan G, Aboushareb F, Slight SP. Preventing sepsis; how can artificial intelligence inform the clinical decision-making process? A systematic review. Int J Med Inform 2021; 150:104457. [PMID: 33878596 DOI: 10.1016/j.ijmedinf.2021.104457] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 03/12/2021] [Accepted: 04/06/2021] [Indexed: 12/13/2022]
Abstract
BACKGROUND AND OBJECTIVES Sepsis is a life-threatening condition that is associated with increased mortality. Artificial intelligence tools can inform clinical decision making by flagging patients at risk of developing infection and subsequent sepsis. This systematic review aims to identify the optimal set of predictors used to train machine learning algorithms to predict the likelihood of an infection and subsequent sepsis. METHODS This systematic review was registered in PROSPERO database (CRD42020158685). We conducted a systematic literature review across 3 large databases: Medline, Cumulative Index of Nursing and Allied Health Literature, and Embase. Quantitative primary research studies that focused on sepsis prediction associated with bacterial infection in adults in all care settings were eligible for inclusion. RESULTS Seventeen articles met our inclusion criteria. We identified 194 predictors that were used to train machine learning algorithms, with 13 predictors used on average across all included studies. The most prevalent predictors included age, gender, smoking, alcohol intake, heart rate, blood pressure, lactate level, cardiovascular disease, endocrine disease, cancer, chronic kidney disease (eGFR<60 mL/min), white blood cell count, liver dysfunction, surgical approach (open or minimally invasive), and pre-operative haematocrit < 30 %. All included studies used artificial intelligence techniques, with average sensitivity 75.7 ± 17.88, and average specificity 63.08 ± 22.01. CONCLUSION The type of predictors influenced the predictive power and predictive timeframe of the developed machine learning algorithm. Predicting the likelihood of sepsis through artificial intelligence can help concentrate finite resources to those patients who are most at risk. Future studies should focus on developing more sensitive and specific algorithms.
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Affiliation(s)
- Nehal Hassan
- School of Pharmacy, Newcastle University, King George VI Building, Newcastle upon Tyne, NE1 7RU, UK.
| | - Robert Slight
- Newcastle Upon Tyne Hospitals NHS Foundation Trust, Freeman Hospital, High Heaton, Newcastle upon Tyne, NE7 7DN, UK.
| | - Daniel Weiand
- Newcastle Upon Tyne Hospitals NHS Foundation Trust, Freeman Hospital, High Heaton, Newcastle upon Tyne, NE7 7DN, UK.
| | - Akke Vellinga
- School of Medicine, National University of Ireland Galway, University Road, Galway, H91 TK33, Ireland.
| | - Graham Morgan
- School of Computing, Newcastle University, Urban Sciences Building, Newcastle upon Tyne, NE4 5TG, UK.
| | - Fathy Aboushareb
- Northumbria Healthcare NHS Foundation Trust, Rake Lane, North Shields, Tyne and Wear, NE29 8NH, UK.
| | - Sarah P Slight
- School of Pharmacy, Newcastle University, King George VI Building, Newcastle upon Tyne, NE1 7RU, UK.
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Gupta A, Liu T, Crick C. Utilizing time series data embedded in electronic health records to develop continuous mortality risk prediction models using hidden Markov models: A sepsis case study. Stat Methods Med Res 2020; 29:3409-3423. [PMID: 32552573 DOI: 10.1177/0962280220929045] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Continuous mortality risk monitoring is instrumental to manage a patient's care and to efficiently utilize the limited hospital resources. Due to incompleteness and irregularities of electronic health records (EHR), developing continuous mortality risk prediction using EHR data is a challenge. In this study, we propose a framework to continuously monitor mortality risk, and apply it to the real-world EHR data. The proposed method employs hidden Markov models (temporal technique) that take account of both the previous state of patient's health and the current value of clinical signs. Following the Sepsis-3 definition, we selected 3898 encounters of patients with suspected infection to compare the performance of temporal and non-temporal methods (Decision Tree (DT), Logistic Regression (LR), Naive Bayes (NB), Random Forest (RF), and Support Vector Machine (SVM)). The area under receiver operating characteristics (AUROC) curve, sensitivity, specificity and G-mean were used as performance measures. On the selected data, the AUROC of the proposed temporal framework (0.87) is 9-12% greater than the nontemporal methods (DT: 0.78, NB: 0.79, SVM: 0.79, LR: 0.80 and RF: 0.80). The results also show that our model (G-mean=0.78) provides a better balance between sensitivity and specificity compared to clinically acceptable bed-side criteria (G-mean=0.71). The proposed framework leverages the longitudinal data available in EHR and performs better than the non-temporal methods. The proposed method facilitates information related to the time of change of the patient's health that may help practitioners to plan early and develop effective treatment strategies.
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Affiliation(s)
- Akash Gupta
- California State University, Northridge, Northridge, CA, USA
| | - Tieming Liu
- Oklahoma State University, Stillwater, Stillwater, OK, USA
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